Efficient Active Quickest Detection for Streaming Data under Sampling Control
Author(s)
Xu, Qunzhi
Advisor(s)
Editor(s)
Collections
Supplementary to:
Permanent Link
Abstract
Quickest detection has a wide range of real-world applications in industrial quality control, biosurveillance, network security, etc. Under a general setting, there are p local streams in a system, and at some unknown time ν, an occurring event impacts s of the available streams by changing the distribution of their samples. In many applications, one often faces the sampling control constraint in the sense of allowing only to sample from q of the p local streams at each time instant. We call this “Active Quickest Detection”. The objective of active quickest detection is to decide how to adaptively sample partial data from these p local streams and how to use the observed partial data to raise a global alarm as quickly as possible once the change occurs subject to both the false alarm and sampling control constraints. This dissertation focuses on making comprehensive progress on methodology, theory, and application of active quickest detection problem to multi-stream data under the sampling or resource constraints. Our specific research aims are to design new algorithms with theoretical guarantees and develop an asymptotic optimality theory to characterize sharp information bound.
Sponsor
Date
2024-06-26
Extent
Resource Type
Text
Resource Subtype
Dissertation